Benchmarks
The best speech emotion recognition model, measured
We built speech-emotion-bench to settle a simple question: which model actually understands emotion in speech? It scores 64,384 held-out clips across roughly 20 languages and 7 emotion classes with one identical pipeline for 27+ systems — open models, frontier APIs, and Oruk Spectra alike. On this set, Oruk Spectra leads at 77.6% accuracy and 0.810 macro F1.
Evaluations · speech-emotion-bench
Best-in-class at understanding people
We built speech-emotion-bench to settle it: 64,384 held-out clips across ~20 languages and seven emotions, one scoring pipeline for all 27+ systems. Oruk Spectra against the best open models and frontier APIs, each given best-chance prompting. It isn't close.
Oruk Spectra · accuracy, 64,384-clip held-out set
* Closed models are scored on a fixed 5,000-clip stratified subsample; rescoring open models on the same subsample shifts results by less than two points. Claude receives transcripts only — it has no audio input. Oruk Spectra is trained on the benchmark's training split; every other system is zero-shot cross-corpus.
Per-emotion F1
Macro F1 for each of the seven emotions, Oruk Spectra against the best frontier API (Gemini 3 Flash Preview). The gap is widest on the emotions text-only and general-audio models miss most.
| Emotion | Oruk Spectra | Best frontier API |
|---|---|---|
| Disgust | 0.905 | 0.185 |
| Fear | 0.864 | 0.355 |
| Surprise | 0.858 | 0.268 |
| Anger | 0.806 | 0.462 |
| Neutral | 0.756 | 0.529 |
| Sadness | 0.746 | 0.313 |
| Happiness | 0.737 | 0.502 |
How we score it
A held-out speech emotion recognition benchmark: 64,384 clips across ~20 languages and 7 emotion classes, scored with one identical pipeline for 27+ systems. Closed/API models are scored on a fixed 5,000-clip stratified subsample; rescoring open models on the same subsample shifts results by less than two points.
Closed models receive best-chance prompting: expert-annotator framing, acoustic definitions for all seven classes, temperature 0, and constrained single-label output. Claude models receive transcripts only — they have no audio input.
One fairness note: Oruk Spectra is trained on the benchmark's training split, while every other system is evaluated zero-shot cross-corpus. The harness and results are public at github.com/Oruk-AI/speech-emotion-bench.